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import gradio as gr |
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import onnxruntime as rt |
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from transformers import AutoTokenizer |
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import torch, json |
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tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") |
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with open("path_files/tags_types_encoded.json", "r") as fp: |
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encode_tags_types = json.load(fp) |
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Tags = list(encode_tags_types.keys()) |
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inf_session = rt.InferenceSession("path_files/Question-classifier-quantized.onnx") |
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input_name = inf_session.get_inputs()[0].name |
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output_name = inf_session.get_outputs()[0].name |
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def classify_Question_tags(Summary): |
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input_ids = tokenizer(Summary)['input_ids'][:512] |
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logits = inf_session.run([output_name], {input_name: [input_ids]})[0] |
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logits = torch.FloatTensor(logits) |
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probs = torch.sigmoid(logits)[0] |
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return dict(zip(Tags, map(float, probs))) |
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label = gr.outputs.Label(num_top_classes=5) |
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iface = gr.Interface(fn=classify_Question_tags, inputs="text", outputs=label) |
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iface.launch(inline=False) |
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